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Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period

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  • Rama K. Malladi

    (California State University)

Abstract

Twenty-five machine learning (ML) methods and ordinary least squares regression (OLS) are trained to detect in-sample U.S. annual inflation rates up to a year in advance. The FRED-MD monthly dataset with 134 economic and financial variables from 1959 to April 2022 is used for training, validation, and forecasting. Out of these twenty-five ML methods, top-ten (by root mean square error or RMSE) are chosen to forecast the out-of-sample annual inflation rate. The ML methods are more accurate than the OLS in forecasting the annual inflation rate. The OLS does not appear in the top-10 list in any forecasting period. The ML methods robustly classify the labor market as the top factor in forecasting inflation. The labor market has a significantly higher impact on inflation than the housing or stock market.

Suggested Citation

  • Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10436-w
    DOI: 10.1007/s10614-023-10436-w
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    More about this item

    Keywords

    Inflation forecast; Financial econometrics; Machine learning;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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